Parameter estimation of biological signals such as the electrocardiogram (ECG) is of key clinical significance and can be used to monitor cardiac health and diagnose heart diseases. However, statistical ECG models with unknown parameters depend upon a priori parameters such as mean cardiac frequency and user-specified parameters such as the number of harmonics in the ECG model. These parameters can vary from patient to patient and with different disease stages. In this paper, we propose a sequential Bayesian tracking method to adaptively select the best cardiac parameters in order to minimize the parameter estimation error. Our results using real ECG data demonstrate the importance of the adaptive algorithm for selecting cardiac parameters at each time instant and show how these parameters can be used to classify different types of ECG signals.